78 citations · 191 across the 14 of their papers we have counts for
25 papers
Self-Augmented Diffusion Guidance for Physics-Informed Generation
Akira Osaka, Naoya Takeishi, Takehisa Yairi
Diffusion models can be used to generate spatiotemporal signals of physical phenomena, such as time-series images of fluid dynamics. However, a major limitation of standard diffusi…
Forecast Sports Outcomes under Efficient Market Hypothesis: Theoretical and Experimental Analysis of Odds-Only and Generalised Linear Models
Kaito Goto, Naoya Takeishi, Takehisa Yairi
Converting betting odds into accurate outcome probabilities is a fundamental challenge in order to use betting odds as a benchmark for sports forecasting and market efficiency anal…
Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
Henrik Krauss, Johann Licher, Naoya Takeishi +2
Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumptions. Model-based approaches require p…
Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
Keisuke Fujii, Kazushi Tsutsui, Yu Teshima +6
Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal m…
A Temporal Difference Method for Stochastic Continuous Dynamics
Haruki Settai, Naoya Takeishi, Takehisa Yairi
For continuous systems modeled by dynamical equations such as ODEs and SDEs, Bellman's Principle of Optimality takes the form of the Hamilton-Jacobi-Bellman (HJB) equation, which p…
Mimicking Better by Matching the Approximate Action Distribution
João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi +1
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. MAAD utilizes a surrogate reward signal, which can be deri…